Digital Analytics & Regression


This course uses a case study approach to take you through the end to end process of identifying a business objective, designing the model to address it, sourcing the data and ultimately arriving at the insights. When you complete this course, you can apply these methods and principles in a variety of contexts, with big, medium or small data.
Intro Video:

About This Course

Follow a case study where you define the business objective, establish the data required to address that objective, and use R, the programming language, to derive insights from the data. As with any business challenge, you will be required to articulate your findings to a business audience.

  • Learn basic concepts in statistics with step-by-step guidance on how to conduct an analysis to solve the business problem.
  • Data Science is like triathlon. Programming is cycling, by far the biggest investment is required in hardware and software. Running is domain expertise and communication skills and, swimming is mathematics, statistics and modelling. There are competitions in each of these disciplines, cycling, running and swimming (and there always will be), but the need for super athletes who can do all 3 is growing. An athlete who is brilliant at one discipline can learn the other two and succeed in the triathlon.

Course Syllabus

    Module 1 – A Case Study Approach to Analytics

    1. Understand the business context
    2. Formulate the business objective
    3. State the hypothesis
    4. Assess available data
    5. Assign data for use
    Module 2 – Data Scientist Workbench

    1. Using Data Scientist Workbench
    2. What is R?
    3. Loading data into R with Data Scientist Workbench
    4. Upload a CSV data file into Data Scientist Workbench and RStudio
    Module 3 – Google Trends Data in R

    1. Access Google Trends data in R
    Module 4 – Simple Linear Regression in R

    1. Regression and Google Trends Data in R
    2. Box Plots and Histograms in R
    3. Scatter Plots & Lines of best fit in R
    4. Simple Linear Regression in R
    Module 5 – Presenting Data Analytics in Business

    1. Using data to answer a business question
    2. Summarizing the data analytics process
    3. Presenting data insights

Recommended skills prior to taking this course

  • Knowledge of basic statistics is an asset
  • Knowledge of basic R is an asset


  • None

Course Staff

Fireside Analytics Inc.

Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on Facebook, Twitter and LinkedIn.

Course Content

Total learning: 1 lesson Time: 8 hours

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